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The human body emits a multitude of volatile organic compounds (VOCs) via tissues and various bodily fluids or exhaled breath. These compounds collectively create a distinctive chemical profile, which can potentially be employed to identify changes in human metabolism associated with colorectal cancer (CRC) and, consequently, facilitate the diagnosis of this disease. The main goal of this study was to investigate and characterize the VOCs’ chemical patterns associated with the breath of CRC patients and controls and identify potential expiratory markers of this disease. For this purpose, gas chromatography–mass spectrometry was applied. Collectively, 1656 distinct compounds were identified in the breath samples provided by 152 subjects. Twenty-two statistically significant VOCs (p-xylene; hexanal; 2-methyl-1,3-dioxolane; 2,2,4-trimethyl-1,3-pentanediol diisobutyrate; hexadecane; nonane; ethylbenzene; cyclohexanone; diethyl phthalate; 6-methyl-5-hepten-2-one; tetrahydro-2H-pyran-2-one; 2-butanone; benzaldehyde; dodecanal; benzothiazole; tetradecane; 1-dodecanol; 1-benzene; 3-methylcyclopentyl acetate; 1-nonene; toluene) were observed at higher concentrations in the exhaled breath of the CRC group. The elevated levels of these VOCs in CRC patients’ breath suggest the potential for these compounds to serve as biomarkers for CRC.

The human body releases numerous volatile organic compounds (VOCs) through tissues and various body fluids, including breath. These compounds form a specific chemical profile that may be used to detect the colorectal cancer CRC-related changes in human metabolism and thereby diagnose this type of cancer. The main goal of this study was to investigate the volatile signatures formed by VOCs released from the CRC tissue. For this purpose, headspace solid-phase microextraction gas chromatography-mass spectrometry was applied. In total, 163 compounds were detected. Both cancerous and non-cancerous tissues emitted 138 common VOCs. Ten volatiles (2-butanone; dodecane; benzaldehyde; pyridine; octane; 2-pentanone; toluene; p-xylene; n-pentane; 2-methyl-2-propanol) occurred in at least 90% of both types of samples; 1-propanol in cancer tissue (86% in normal one), acetone in normal tissue (82% in cancer one). Four compounds (1-propanol, pyridine, isoprene, methyl thiolacetate) were found to have increased emissions from cancer tissue, whereas eleven showed reduced release from this type of tissue (2-butanone; 2-pentanone; 2-methyl-2-propanol; ethyl acetate; 3-methyl-1-butanol; d-limonene; tetradecane; dodecanal; tridecane; 2-ethyl-1-hexanol; cyclohexanone). The outcomes of this study provide evidence that the VOCs signature of the CRC tissue is altered by the CRC. The volatile constituents of this distinct signature can be emitted through exhalation and serve as potential biomarkers for identifying the presence of CRC. Reliable identification of the VOCs associated with CRC is essential to guide and tune the development of advanced sensor technologies that can effectively and sensitively detect and quantify these markers.

It has been shown that the gut microbiota plays a central role in human health and disease. A wide range of volatile metabolites present in exhaled breath have been linked with gut microbiota and proposed as a non-invasive marker for monitoring pathological conditions. The aim of this study was to examine the possible correlation between volatile organic compounds (VOCs) in exhaled breath and the fecal microbiome by multivariate statistical analysis in gastric cancer patients (n = 16) and healthy controls (n = 33). Shotgun metagenomic sequencing was used to characterize the fecal microbiota. Breath-VOC profiles in the same participants were identified by an untargeted gas chromatography–mass spectrometry (GC–MS) technique. A multivariate statistical approach involving a canonical correlation analysis (CCA) and sparse principal component analysis identified the significant relationship between the breath VOCs and fecal microbiota. This relation was found to differ between gastric cancer patients and healthy controls. In 16 cancer cases, 14 distinct metabolites identified from the breath belonging to hydrocarbons, alcohols, aromatics, ketones, ethers, and organosulfur compounds were highly correlated with 33 fecal bacterial taxa (correlation of 0.891, p-value 0.045), whereas in 33 healthy controls, 7 volatile metabolites belonging to alcohols, aldehydes, esters, phenols, and benzamide derivatives correlated with 17 bacterial taxa (correlation of 0.871, p-value 0.0007). This study suggested that the correlation between fecal microbiota and breath VOCs was effective in identifying exhaled volatile metabolites and the functional effects of microbiome, thus helping to understand cancer-related changes and improving the survival and life expectancy in gastric cancer patients.

Background: Volatilomics is a powerful tool capable of providing novel biomarkers for medical diagnosis and therapy monitoring. The objective of this study is to identify potential volatile biomarkers of gastric cancer. Methods: The volatilomic signatures of gastric tissues obtained from two distinct populations were investigated using gas chromatography with mass spectrometric detection. Results: Amongst the volatiles emitted, nineteen showed differences in their headspace concentrations above the normal and cancer tissues in at least one population of patients. Headspace levels of seven compounds (hexanal, nonanal, cyclohexanone, 2-nonanone, pyrrole, pyridine, and phenol) were significantly higher above the cancer tissue, whereas eleven volatiles (ethyl acetate, acetoin, 2,3-butanedione, 3-methyl-1-butanol, 2-pentanone, γ-butyrolactone, DL-limonene, benzaldehyde, 2-methyl-1-propanol, benzonitrile, and 3-methyl-butanal) were higher above the non-cancerous tissue. One compound, isoprene, exhibited contradictory alterations in both cohorts. Five compounds, pyridine, ethyl acetate, acetoin, 2,3-butanedione, and 3-methyl-1-butanol, showed consistent cancer-related changes in both populations. Conclusions: Pyridine is found to be the most promising biomarker candidate for detecting gastric cancer. The difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes, or pathways. The results of this study confirm that the chemical fingerprint formed by volatiles in gastric tissue is altered by gastric cancer.

The continuous search for new sensing materials with high recognition capabilities is necessary to improve existing analytical procedures and to develop new ones. Natural glassy carbon and polydimethylsiloxane were shown to be used for the preparation of carbon paste electrodes to employ them in new, voltammetric, green-chemistry-friendly electroanalytical procedures aimed at evaluating the antioxidant capacity of plant extracts, dietary supplements, and hydrolats. The developed electrodes provided well-shaped and reproducible voltammetric signals (RSD = 1%) of the oxidation of epigallocatechin gallate, the main component of many plants and plant-based formulations with antioxidative activity, in the 1–12.5 µM range (DPV mode, LOD = 0.08 µM). If needed, the performance of new carbon paste electrodes can be further enhanced by the introduction of trivalent rare earth oxides to carbon paste to increase its active surface, facilitate electron transfer, and improve the resolution of recorded signals.

In vitro studies can help reveal the biochemical pathways underlying the origin of volatile indicators of numerous diseases. The key objective of this study is to identify the potential biomarkers of gastric cancer. For this purpose, the volatilomic signatures of two human gastric cancer cell lines, AGS (human gastric adenocarcinoma) and SNU-1 (human gastric carcinoma), and one normal gastric mucosa cell line (GES-1) were investigated. More specifically, gas chromatography mass spectrometry has been applied to pinpoint changes in cell metabolism triggered by cancer. In total, ten volatiles were found to be metabolized, and thirty-five were produced by cells under study. The volatiles consumed were mainly six aldehydes and two heterocyclics, whereas the volatiles released embraced twelve ketones, eight alcohols, six hydrocarbons, three esters, three ethers, and three aromatic compounds. The SNU-1 cell line was found to have significantly altered metabolism in comparison to normal GES-1 cells. This was manifested by the decreased production of alcohols and ketones and the upregulated emission of esters. The AGS cells exhibited the increased production of methyl ketones containing an odd number of carbons, namely 2-tridecanone, 2-pentadecanone, and 2-heptadecanone. This study provides evidence that the cancer state modifies the volatilome of human cells.

The presence of certain volatile biomarkers in the breath of patients with gastric cancer has been reported by several studies; however, the origin of these compounds remains controversial. In vitro studies, involving gastric cancer cells may address this problem and aid in revealing the biochemical pathways underlying the production and metabolism of gastric cancer volatile indicators. Gas chromatography with mass spectrometric detection, coupled with headspace needle trap extraction as the pre-concentration technique, has been applied to map the volatilomic footprints of human HGC-27 and CLS-145 gastric cancer cell lines and normal Human Stomach Epithelial Cells (HSEC). In total, 27 volatile compounds are found to be associated with metabolism occurring in HGC-27, CLS-145, and HSEC. Amongst these, the headspace concentrations of 12 volatiles were found to be reduced compared to those above just the cultivating medium, namely there was an observed uptake of eight aldehydes (2-methylpropanal, 2-methyl-2-propenal, 2-methylbutanal, 3-methylbutanal, hexanal, heptanal, nonanal, and benzaldehyde), three heterocyclic compounds (2-methyl-furan, 2-ethyl-furan, and 2-pentyl-furan), and one sulfur-containing compound (dimethyl disulphide). For the other 15 volatiles, the headspace concentrations above the healthy and cancerous cells were found to be higher than those found above the cultivating medium, namely the cells were found to release three esters (ethyl acetate, ethyl propanoate, and ethyl 2-methylbutyrate), seven ketones (2-pentanone, 2-heptanone, 2-nonanone, 2-undecanone, 2-tridecanone, 2-pentadecanone, and 2-heptadecanone), three alcohols (2-methyl-1-butanol, 3-methyl-1-butanol, and 2-ethyl-1-hexanol), one aromatic compound (toluene), and one sulfur containing compound [2-methyl-5-(methylthio) furan]. In comparison to HSEC, HGC-27 cancer cell lines were found to have significantly altered metabolism, manifested by an increased production of methyl ketones containing an odd number of carbons. Amongst these species, three volatiles were found exclusively to be produced by this cell line, namely 2-undecanone, 2-tridecanone, and 2-heptadecanone. Another interesting feature of the HGC-27 footprint is the lowered level of alcohols and esters. The CLS-145 cells exhibited less pronounced changes in their volatilomic pattern compared to HSEC. Their footprint was characterized by the upregulated production of esters and 2-ethyl-hexanol and downregulated production of other alcohols. We have therefore demonstrated that it is possible to differentiate between cancerous and healthy gastric cells using biochemical volatile signatures.

Researchers looking for biomarkers from different sources, such as breath, urine, or blood, frequently search for specific patterns of volatile organic compounds (VOCs), often using pattern recognition or machine learning techniques. However, they are not generally aware that these patterns change depending on the source they use. Therefore, we have created a simple model to demonstrate that the distribution patterns of VOCs in fat, mixed venous blood, alveolar air, and end-tidal breath are different. Our approach follows well-established models for the description of dynamic real-time breath concentration profiles. We start with a uniform distribution of end-tidal concentrations of selected VOCs and calculate the corresponding target concentrations. For this, we only need partition coefficients, mass balance, and the assumption of an equilibrium state, which avoids the need to know the volatiles’ metabolic rates and production rates within the different compartments.

Soft chemical ionization mass spectrometric techniques, such as proton transfer reaction mass spectrometry (PTR-MS), are often used in breath analysis, being particularly powerful for real-time measurements. To ascertain the type and concentration of volatiles in exhaled breath clearly assignable product ions resulting from these volatiles need to be determined. This is difficult for compounds where isomers are common, and one important class of breath volatiles where this occurs are ketones. Here we present a series of extensive measurements on the reactions of H3O+ with a selection of ketones using PTR-MS. Of particular interest is to determine if ketone isomers can be distinguished without the need for pre-separation by manipulating the ion chemistry through changes in the reduced electric field. An additional issue for breath analysis is that the product ion distributions for these breath volatiles are usually determined from direct PTR-MS measurements of the compounds under the normal operating conditions of the instruments. Generally, no account is made for the effects on the ion-molecule reactions by the introduction of humid air samples or increased CO2 concentrations into the drift tubes of these analytical devices resulting from breath. Therefore, another motivation of this study is to determine the effects, if any, on the product ion distributions under the humid conditions associated with breath sampling. However, the ultimate objective for this study is to provide a valuable database of use to other researchers in the field of breath analysis to aid in analysis and quantification of trace amounts of ketones in human breath. Here we present a comprehensive compendium of the product ion distributions as a function of the reduced electric field for the reactions of H3O+. (H2O)n (n = 0 and 1) with nineteen ketones under normal and humid (100% relative humidity for 37 °C) PTR-MS conditions. The ketones selected for inclusion in this compendium are (in order of increasing molecular weight): 2-butanone; 2-pentanone; 3-pentanone; 2-hexanone; 3-hexanone; 2-heptanone; 3-heptanone; 4-heptanone; 3-octanone; 2-nonanone; 3-nonanone; 2-decanone; 3-decanone; cyclohexanone; 3-methyl-2-butanone; 3-methyl-2-pentanone; 2-methyl-3-pentanone; 2-methyl-3-hexanone; and 2-methyl-3-heptanone.